The goal of image fusion is to integrate complementary multisensor, multitemporal and/or multiview information into one new image containing information the quality of which cannot be achieved otherwise. The term "quality", its meaning and measurement depend on the particular application. In this tutorial, we categorize the image fusion methods according to the data entering the fusion and according to the fusion purpose. We distinguish the following categories.
- Multiview fusion of images from the same modality and taken at the same time but from different viewpoints.
- Multimodal fusion of images coming from different sensors (visible and infrared, PET and CT/NMR, panchromatic and multispectral satellite images).
- Multitemporal fusion of images taken at different times in order to detect changes between them or to synthesize realistic images of objects which were not photographed in a desired time.
- Multifocus fusion of images of a 3D scene taken repeatedly with various focal length.
- Fusion for image restoration. Fusing two or more images of the same scene and modality, each of them blurred and noisy, may lead to a deblurred and denoised image. Multichannel blind deconvolution is a typical representative of this category. We discuss not only the traditional case of spatially invariant image blurring but also the more realistic case of space-variant blur. This approach can be extended to superresolution fusion, where input blurred images of low spatial resolution are fused to provide us a high-resolution image. This fusion category is very important for producers and users of low-resolution imaging devices such as mobile phones, camcorders, web cameras, and security and surveillance cameras, because it can be used not only for still images but also for video restoration. This category form the core of the tutorial and will be covered in detail.
In each fusion category, we explain basic principles, briefly review known methods, and provide examples from different application areas. No prior knowledge of the field is required of attendees. However, more in-depth discussion will continue in the last category of fusion for image restoration. We will guide the attendees through this category providing some mathematical description of image restoration methods, and we conclude by many interesting practical examples including live demo of our MATLAB Toolboxes.
The target audience of the tutorial are researchers from all application areas who need to integrate and fuse image data of various kind as well as the specialists in image fusion interested in a new development of this field.